merge_dataset.py

from datasets import load_dataset, concatenate_datasets, Audio
from glob import glob
from datasets import load_from_disk

MAX_ROWS = 6000
FINAL = []


# -------------------------------------------------
# Helper
# -------------------------------------------------
def load_local_parquet(path, text_col="transcription", filter_fn=None):
    print(f"\n📌 Loading: {path}")
    files = sorted(glob(path, recursive=True))
    assert files, f"No parquet files found in {path}"

    ds = load_dataset(
        "parquet",
        data_files=files,
        split="train"
    )

    if filter_fn:
        ds = ds.filter(filter_fn)

    if text_col != "text":
        ds = ds.rename_column(text_col, "text")

    # 🔥 KEEP ONLY audio + text
    ds = ds.select_columns(["audio", "text"])

    ds = ds.cast_column("audio", Audio(sampling_rate=24000))

    if len(ds) > MAX_ROWS:
        ds = ds.shuffle(seed=42).select(range(MAX_ROWS))

    print(f"✅ Rows used: {len(ds)} | Columns: {ds.column_names}")
    return ds
def load_arrow_dataset(path, text_col="transcription"):
    print(f"\n📌 Loading Arrow dataset: {path}")

    ds = load_from_disk(path)

    if text_col != "text":
        ds = ds.rename_column(text_col, "text")

    # 🔥 KEEP ONLY audio + text
    ds = ds.select_columns(["audio", "text"])

    ds = ds.cast_column("audio", Audio(sampling_rate=24000))

    if len(ds) > MAX_ROWS:
        ds = ds.shuffle(seed=42).select(range(MAX_ROWS))

    print(f"✅ Rows used: {len(ds)} | Columns: {ds.column_names}")
    return ds

# -------------------------------------------------
# 1. Bengali (male only)
# -------------------------------------------------
#bengali = load_local_parquet(
#    "local_data/IndicTTS_Bengali/data/**/*.parquet",
#    text_col="text",
#    filter_fn=lambda x: "train_bengalimale" in x["utterance_id"]
#)
#FINAL.append(bengali)


# -------------------------------------------------
# 2. Arabic
# -------------------------------------------------
#arabic = load_local_parquet(
#    "local_data/arabic_tts/**/*.parquet",
#    text_col="transcription"
#)
#FINAL.append(arabic)


# -------------------------------------------------
# 3. Hindi (female 5hr)
# -------------------------------------------------
hindi = load_local_parquet(
    "local_data/hindi_female_5hr/**/*.parquet",
    text_col="text"
)
FINAL.append(hindi)


# -------------------------------------------------
# 4. English (local)
# -------------------------------------------------
#english = load_arrow_dataset(
#    "local_data/local_eng",
#    text_col="transcription"
#)
#FINAL.append(english)


# -------------------------------------------------
# 5. Punjabi (local)
# -------------------------------------------------
#punjabi = load_arrow_dataset(
#    "local_data/local_punjabi/train",
#    text_col="transcription"
#)
#FINAL.append(punjabi)


# -------------------------------------------------
# Merge ALL
# -------------------------------------------------
print("\n🚀 Merging all datasets")
merged = concatenate_datasets(FINAL)

print("\n📊 FINAL DATASET")
print(merged)
print("Total rows:", len(merged))
print("Columns:", merged.column_names)


# -------------------------------------------------
# Save locally
# -------------------------------------------------
OUT_DIR = "data/dataset_hindi_6k"
merged.save_to_disk(OUT_DIR)

print(f"\n💾 Saved with save_to_disk → {OUT_DIR}")


# Optional: also save as Parquet (Trainer-friendly)
#merged.to_parquet(f"{OUT_DIR}.parquet")
print(f"💾 Saved Parquet → {OUT_DIR}.parquet")